The present invention generally relates to wireless communication devices, and particularly relates to selecting a subset of modeled impairment correlation terms for use in determining a composite impairment correlation term.
Signals transmitted in a wireless communication system such as Code Division Multiple Access (CDMA) or Wideband CDMA (WCDMA) systems are subjected to multiple sources of interference and noise as they propagate via radio channels. The various sources of interference and noise that affect signal transmission and reception in a wireless communication system are broadly referred to as impairments. For example, in dispersive environments, signals may become impaired due to multi-path fading, where reflections cause several instances of the same transmitted signal to arrive at a receiver at different points in time. Other sources of signal impairments include own-cell and other-cell interference, where the term ‘cell’ refers to a geographic area of radio coverage served by a particular cell site (e.g., a radio tower and corresponding equipment such as a base station). Own-cell interference arises primarily due to multi-path propagation, since spreading code orthogonality is lost due to inter-symbol interference, multi-user interference, or both. Other-cell interference arises mainly due to the use of different scrambling codes assigned to different users in adjacent cells. The different scrambling codes cause energy received from a neighboring cell to act as interference in one's own cell, the interference being colored for multipath fading channels.
Various other signal impairment sources may also be present. For example, in multi-antenna systems such as Multiple-Input Multiple-Output (MIMO) systems, additional users may be simultaneously served via assigned substreams. However, each substream yields a different interference pattern. In addition, each user's substream may be pre-coded to match its channel realization, e.g., by using a precoding vector acquired from a codebook of such vectors. Each preceding vector applied at a transmitter gives rise to a distinct interference pattern.
In some multi-antenna systems, transmit diversity is used to achieve high data rates by transmitting the same signal stream via multiple transmit antennas. Transmit diversity methods conventionally fall into two categories: open and closed loop. Space-Time Transmit Diversity (STTD) is an example of an open loop technique where a signal stream is space-time coded over two symbol streams and transmitted via two antennas simultaneously. This results in a signal impairment pattern that is different from that caused by single-antenna transmission.
For closed loop transmit diversity, such as Transmit Adaptive Array (TXAA), a receiver measures relative phase and power of multiple pilot channels, e.g., CPICH common pilot signals. The measured information is fed back to the corresponding base station. The base station adjusts its signal transmission characteristics based on this information in order to maximize received power at the receiver. A number of distinct transmit patterns may be used by a closed loop transmitter, e.g., one pattern per assigned user. Each of these different patterns will also be a distinct source of signal impairment.
Unknown or un-modeled signal impairments are conventionally treated as white noise. Also, certain types of impairments may be correlated. That is, two impairment signals may in fact be related, and thus are said to be correlated. Some conventional receivers such as Generalized-RAKE (G-RAKE) receivers and Chip Equalizers (CEQs) make use of these impairment correlations to improve received signal processing.
A G-RAKE receiver includes various ‘fingers’, each finger having an assigned path delay for receiving a particular image of a multipath signal and a correlator for despreading the received image. In combination, the fingers despread multiple signal images of a received multipath signal, thus mitigating the effect of the multipath channel fading phenomenon. Some G-RAKE fingers may be placed on signal path delays for receiving images of a multipath signal while other fingers may be placed off path delays for capturing impairment correlation information associated with the various fingers. The finger outputs are weighted and coherently combined to improve received signal demodulation and/or signal-to-interference estimation. The weights assigned to the finger outputs are conventionally a function of the channel characteristics and impairment correlations. As such, knowledge of signal impairments may be used to improve received signal processing. In a similar manner, CEQs utilize impairment correlations for improving received signal processing where the placement of filter taps in a CEQ is comparable to the placement of fingers in a G-RAKE.
According to one conventional approach, the dominant interference-based impairment correlation term is modeled and expressed as one impairment correlation matrix while the remaining interferers and noise-related impairments are lumped into a second impairment correlation matrix. These component impairment correlation matrices are then subjected to a model fitting process, e.g., a least-squares process to determine corresponding model fitting parameters. The model fitting parameters determine the weight or contribution the noise and interference-based impairment correlation terms have on the corresponding composite matrix.
According to another conventional approach, an impairment correlation term is modeled separately for each impairment source of interest. As such, multiple noise and interference-based impairment correlation terms are generated. All of the various impairment correlation terms are then combined to form a composite impairment correlation matrix, e.g., using a least-squares process. As with the conventional ‘lumping’ approach, calculated model fitting parameters correspond to the weight or contribution each impairment correlation term has on the composite matrix.